Operationalizing Global Workspace Theory for Metacognitive Multi-Agent LLM Architecture

Monolithic Large Language Models (LLMs) operate fun-damentally as System 1 intuitive predictors, lacking built-in mechanisms to pause, debate, or audit their own rea-soning chains. When deployed in unconstrained multi-agent loops, these models suffer from context window di-lution, compounding hallucinations, and consensus bias(mode collapse). In cognitive neuroscience, human con-sciousness and high-level metacognitive reasoning aremodeled by Global Workspace Theory (GWT), whichposits that a capacity-limited central workspace inte-grates, selects, and globally broadcasts information gen-erated by parallel, specialized, subconscious neural pro-cessors.In this paper, we introduce SynapseFabric(NeuroSyn), a cognitive architecture with a production-oriented implementation that operationalizes GWT intoa software framework for multi-agent LLM orchestra-tion. By establishing a direct 1:1 mapping betweenneurobiological constructs and computational primi-tives, SynapseFabric coordinates modular sub-agentsthrough a Parallel Realities Architecture (PRA) gov-erned by a central selection and global broadcast hub(SynthesizerBoss). We detail the mathematical for-malisms of neuromodulatory intent vectors, stochasticdivergence injection, vector-space diversity pruning, andcomposite salience scoring. Furthermore, we evaluateSynapseFabric across three illustrative deploymentcase studies. We demonstrate that GWT-structuredorchestration substantially reduces hallucinated outputs,provides deterministic epistemic control, and yieldsaudit-ready decision provenance.